The invention relates to a method for developing and implementing a model for the formal description of a collaborative system comprised of multiple distributed components.
Recent production technologies show a worldwide trend both toward small and medium batch sizes and toward product families of greater diversity. This tendency frequently is in opposition to the requirement for improved productivity in the sense of a decrease in production time and a simultaneous increase in the degree of utilization of machinery.
From the state of the art, industrially flexible production systems are known which possess the capability of achieving a broad palette of different product families or different product types efficiently and with minimal changes to their production environment. However, flexibility concepts of this type require complex design methods and control systems, as the degree of flexibility of the entire system is dependent not only upon the flexibility of the individual components of the production system, but to a much greater degree upon the automation system upon which the production system is based and, e.g., upon its intelligence, its architecture, its interfaces, and its distribution throughout the whole system.
Interactions and complicated links exist between a production system and its associated control system with respect to their structure and behavior.
At the present time, the methods and processes related to cost reduction and a detailed design process for an industrially flexible production system are inadequate.
In the article by Süssmann, B., i.a.: Configuration of agent-oriented manufacturing systems using a petri net-based simulation approach, in: Simulation in Industry '2001, 13th ESS'2001. SCS Europe Bvba, Oct. 18-20 2001, pp 929-933), a method for configuring an agent-based manufacturing system using a formulation based upon a petri net simulation is described. In this, petri net models of hardware components for the manufacturing system are compiled to form a so-called coordination-control model. From the coordination-control model, as a result of analysis using discrete event-oriented simulation processes, information for describing the system and for optimizing both the system layout and the manufacturing process are then derived.
At the same time, the coordination-control model forms a skeleton for a discrete, event-oriented control structure, from which information for the control system can be derived.
Finally, a formulation designed for linking the petri net-based simulation of the hardware of a production or manufacturing system with agent-oriented manufacturing control technology is described. In this process method also, first a coordination-control model is generated from the models of the individual hardware components of the given manufacturing system. From that model, a multi-agent control platform for the manufacturing system is then generated. From this overall system, agent-based decisions can be made, and interaction patterns among the agents for controlling the manufacturing process can be designed. Analysis is accomplished via simulation processes.
An article by A. W. Colombo, “Integration of High-Level Petri Net-Based Formal Methods for the Supervision of Flexible Production Systems”, Tutorial Lecture at the 1st Online Symposium of Electronics Engineers, 2001, addresses tools and methods for the design, testing, and implementation of a flexible production system, however it must be noted that these tools and methods differ from those used in the design, testing, and implementation of the associated control system.
The above-described formulations are suitable only for modeling and describing already known manufacturing systems, i.e. known with respect to their layout and their hardware components, and if necessary for deriving software structures in a second phase.
The articles by K. Feldmann, A. W. Colombo, i.a., “Specification Design and Implementation of Logic Controllers Based on Colored Petri Net Models and the Standard IEC 1131 Part II: Specification and Design” (IEEE, November 1999, VOL 7, pp 657-665), and K. Feldmann, A. W. Colombo, i.a., “Specification, Design and Implementation of Logic Controllers Based on Colored Petri Net Models and the Standard IEC 1131 part II: Design and Implementation” (IEEE November 1999, VOL 7, pp 666-674), deal with the description and the design as well as the implementation of control systems based upon colored petri nets. Another article by A. W. Colombo and R. Carelli, “Petri Nets for Determining Manufacturing Systems” from Sypros G. Tzafestas “Computer-Assisted Management and Control of Manufacturing Systems”, Springer Publishers, the content of which is included in this application, relates to the use of petri nets to simulate production systems. No reference to multi-agent-based control systems is made in the articles.
Without the proper control software a production system is useless. It is the control software that organizes production and plans and synchronizes the allocation of resources. Furthermore, the reliability and the degree of flexibility of the production system is determined not only by the reliability and flexibility of the mechatronic components such as the work station and the warehousing, handling and transport systems, but also to a significant degree by the reliability and flexibility of the control system. Due to the large-scale interaction among the various components of the production system and the multitude of executed functions, a control system for an intelligent production system according to the current state of the art is designed and implemented separately from the production system.
Specifically, the implementation of a state of the art, agent-based control system is performed manually. Here an interactive process is used, customarily comprising a number of steps. In addition, the accuracy and/or precision of the design can be confirmed only when the implementation of the flexible production system has been completed. Because the control software is created separately from the design and implementation of the production system it is to control, the entire process requires a great deal of time, leads easily to misunderstandings and errors, and as a result is, for the most part, highly cost intensive.
From the German patent application 102 24 650 a process in known in which, using a discrete, event-oriented formal simulation process, preferably one based upon high-level petri nets, hereinafter called HLP nets, and/or expanded colored petri nets, hereinafter called KOLP nets, the entire development process for an industrially flexible production system and its multi-agent-based control system is supported from the requirements analysis phase, through modeling, up to validation. Such processes are designed to link models with reality, i.e. for example to process sensor signals that are used in simulation.
In this, the modeling and validation of the flexible production system and its multi-agent-based control system are integrated within a single design phase.
HLP nets as formal methods for creating models for an industrially flexible production system are characterized especially in that the models generated using these methods can be directly transformed to models of a multi-agent-based control system logic, and can subsequently be implemented. In addition, it is guaranteed that the demand for applicability and flexibility of the overall system will be met.
Assuming a predetermined hardware and software configuration for an industrially flexible production system, and based upon the information regarding the tasks and functions that must be realized within the system, the following preferred process method is proposed:                Generation of a simulation model, preferably based upon HLP nets for each component of the flexible production system, accounting for sequence-based specifications,        Generation of a simulation model for interfaces such as communication interfaces and/or sensor-actuator interfaces for each component within a logic control structure derived from the simulation model, accounting for mechatronic specifications,        Validation of the specifications for each modeled component and the associated control structure,        Generation of a coordination model of the components and/or the agents based upon the specifications of a layout of the flexible production system, preferably accounting for both contentious and cooperative behavior among the components, wherein the coordination model serves as the basis for the multi-agent-based control structure of the flexible production system.        
From the validation of structural and dynamic specifications for the multi-agent-based control structure and/or the layout of the flexible production system, and the behavior of the flexible production system, information is received, especially with respect to sequence strategies, conflicts, autonomous and/or cooperative decision-making processes, coordination, contention, product specification and/or control commands, etc. that may occur in the system. This is followed by the generation, testing and optimization of specifications for the multi-agent-based control structure and the associated sequence control strategy for the flexible production system that is to be controlled.
With regard to the process step in which a coordination model for the components and/or the agents is generated based upon specifications for the layout of the flexible production system, it should be noted that in this an agentification of a flexible production system from the HLP net-simulation model is continuously derived. The coordination model serves as the basis for the multi-agent-based control structure of the flexible production system.
Every simulation model contains structural and dynamic characteristics of the modeled production components. If the coordination model is generated from the individual models, both physical and logical interactions among the production components are observed. From the point of view of the production system, the coordination model is a representation of the hardware (production system). From the point of view of the control system, the coordination model is a representation of the control system (topology+intelligence).
The physical interactions are characterized in the implementation phase of the multi-agent-based control system by the future exchange of mechatronic signals between the control system and the production system.
The logic interactions are based predominantly on production specifications and/or production strategies, which the multi-agent-based control system must control and monitor. The quality of the control and monitoring activities is characterized by how much intelligence the control system has (intelligence in the sense of optimal and/or pseudo-optimal decisions in the solution of conflicts). The HLP net-based simulation models represent a skeleton of the multi-agent-based control system, and they are capable of identifying conflicts, providing the necessary production and control information for the decision-making process, and especially validating and/or optimizing the outcome of the solution of conflicts. The result of this is that the simulation model not only represents the multi-agent-based control structure, but also allows the design and/or the optimization of the behavior of this multi-agent-based structure, which is emphasized as a particular advantage of the process of the invention.
The formulation presented above describes a formal process for modeling and validating agent-based control systems for flexible production systems using HLP net models. With this, an initial identification of functionally intelligent units is possible, in other words hardware and software units as intelligent elements in a distributed, network-based production system. The units can be identified over the course of their life cycle and in their various hierarchy levels, in other words ranging from an individual element in the overall assembly up to the entire production system. The positioning of the units (hierarchy level) and their distribution within the overall system is referred to as heterarchy. A distributed and hierarchical system can thus be defined as a heterarchical system.